Computational models of cortical map development have been able
to explain many experimental findings from visual and other sensory
cortices, including columnar organization, receptive field properties,
lateral and feedback connection patterns, and adult plasticity.

The talks and discussion in this workshop will provide a brief
survey of existing and future map modeling approaches, examining:

How to incorporate recent experimental results in models, such
as highly detailed 2-photon imaging data, dramatic species differences
in cortical maps, long-ranging map-specific feedback connections, and
maps for color preference.

How to extend map models to cover topics not well explained by
current approaches, such as the laminar organization within each area,
the development of multiple areas, non-visual modalities, and the
integration of multiple sensory areas.

How to differentiate between different models of the same
phenomena, finding concrete predictions of each model that can be
tested by experiment.

Other current topics related to cortical map development, or
neural maps in general, are also very welcome.

Format:

Informal mini-symposium. An overview talk and informal research
and discussion-oriented talks from a series of speakers are planned,
with questions and comments encouraged throughout.

CNS meeting attendees are welcome to speak briefly about their
own work or other current topics on an informal basis. Please just
tell the organizer (Jim Bednar) what you would like to do, either by
email to jbednar@inf.ed.ac.uk
or verbally at the start of the workshop or during the CNS meeting.
Prior contact is important only if you need to reserve time for a
prepared presentation.

Presenters:

Currently scheduled presentations
include:

James A. Bednar: Cortical Map Development

N. Michael Mayer and M. Asada: Feature Models and Feature MetricsExperimental data indicates that cortical feature maps (CFMs) of
sensiorial areas are topological representations of the set of
experiences of the particular sense. Hence, the layout of these
maps is a result of an optimization and learning process upon
the stimulus set. It is plausible to assume that the layout of
the maps is optimal with respect to some hitherto unknown
function of CFMs. Thus, developmental models have an internal
representation of the continuous feature space and they
(sometimes implicitly) assume a distance measure upon this
feature space. We discuss the role of the design of the feature
space and the way distances are defined within this space. Using
the example of the presumably best investigated cortical area,
the visual cortex, we focus on mutual couplings and trade-off
relationships between features that are reflected by the metrics
of the feature space. We detail two examples: spatial phase
versus retinotopy and the coupling between orientation; and
position in the visual field. Finally, we discuss in what way
the development in the visual cortex may be affected by
adaptation and maturation processes in the retina and the
lateral geniculate nucleus (LGN) and what consequences this
might have for feature models of the visual cortex.

Alessio Plebe: Modeling the Development of the Ventral Visual StreamDespite the visual system being largely holistic, there are
conventional simplifications that allow the study of some of its
main components relatively independently. One example is the
well known paradigm of a ventral stream in the mammalian visual
system, responsible for the recognition of objects, set up
against a dorsal stream, engaged in the spatial analysis.
Always keeping in mind that this dichotomy is mainly a
convenient working convention, one can take advantage of a
segregated ventral stream in trying to modeling its cortical
components up to a certain degree of detail. The fascination of
this attempt is in the chance of tracking the processing path
from sensorial experience down to meaning, walking across one of
the great mysteries of neurophilosophy: how neurons turn from
processing signals into representing reality. Today it is a
unique opportunity, since no other modality has a similar level
of neurophysiological knowledge. In particular, it will be
interesting to experiment how functions contributing to
recognition may emerge during the development of the visual
system, in the different maps that can be included in the model.
It is well known that there is a large gap between the amount of
knowledge gathered up to now on the primary visual area and all
the other areas in the ventral stream, concerning both the
mature functions and the development. This imbalance will be
reflected in a model too, however there is a recent trend in
focusing neuroscientific investigation to other areas, so that
the overall picture may improve rather quickly. A model of the
human ventral stream will be presented, based on a unified
artificial cortical architecture, LISSOM, which is the right
compromise between details of the included neural mechanisms,
and computational simplicity, for modeling highly complex tasks
like recognition. The possibility will be discussed of adding
other less biological plausible modules, like SOM, for the
purpose of extracting meaningful and comparable results from the
model. Considerations will be made on the features expected in
the different visual areas, from the current knowledge, and the
conditions under which similar features develop in the
model. The reproduction of visual experience will be
discussed. Finally it will be shown how lesioned versions of the
model can help in investigating the role of its components in
the recognition task.